

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 			_SCREAMING_SNAKE_CASE (   _A					,       unittest.TestCase		):







   '''simple docstring'''




   lowerCamelCase__				=      KandinskyVaaControlnetImgaImgPipeline
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "image", "hint"]
   lowerCamelCase__				=      ["image_embeds", "negative_image_embeds", "image", "hint"]
   lowerCamelCase__				=      [
       "generator",
       "height",
       "width",
       "strength",
       "guidance_scale",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   lowerCamelCase__				=      False





   @property
   def      _snake_case		(       self					:			List[str]			):
       return 32





   @property
   def      _snake_case		(       self					:			str			):
       return 32





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return self.time_input_dim





   @property
   def      _snake_case		(       self					:			Any			):
       return self.time_input_dim * 4





   @property
   def      _snake_case		(       self					:			int			):
       return 100





   @property
   def      _snake_case		(       self					:			Optional[int]			):
       torch.manual_seed(0			)

       SCREAMING_SNAKE_CASE										=   {
           "in_channels": 8,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image_hint",
           "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
           "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
           "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
           "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
           "layers_per_block": 1,
           "encoder_hid_dim": self.text_embedder_hidden_size,
           "encoder_hid_dim_type": "image_proj",
           "cross_attention_dim": self.cross_attention_dim,
           "attention_head_dim": 4,
           "resnet_time_scale_shift": "scale_shift",
           "class_embed_type": None,
       }

       SCREAMING_SNAKE_CASE										=   UNetaDConditionModel(**UpperCamelCase__			)
       return model





   @property
   def      _snake_case		(       self					:			Tuple			):
       return {
           "block_out_channels": [32, 32, 64, 64],
           "down_block_types": [
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "DownEncoderBlock2D",
               "AttnDownEncoderBlock2D",
           ],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 12,
           "out_channels": 3,
           "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
           "vq_embed_dim": 4,
       }





   @property
   def      _snake_case		(       self					:			Union[str, Any]			):
       torch.manual_seed(0			)
       SCREAMING_SNAKE_CASE										=   VQModel(**self.dummy_movq_kwargs			)
       return model





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   self.dummy_unet
       SCREAMING_SNAKE_CASE										=   self.dummy_movq

       SCREAMING_SNAKE_CASE										=   {
           "num_train_timesteps": 1000,
           "beta_schedule": "linear",
           "beta_start": 0.00_085,
           "beta_end": 0.012,
           "clip_sample": False,
           "set_alpha_to_one": False,
           "steps_offset": 0,
           "prediction_type": "epsilon",
           "thresholding": False,
       }

       SCREAMING_SNAKE_CASE										=   DDIMScheduler(**UpperCamelCase__			)

       SCREAMING_SNAKE_CASE										=   {
           "unet": unet,
           "scheduler": scheduler,
           "movq": movq,
       }

       return components





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			Dict     , __lowerCamelCase					:			Any=0			):
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(UpperCamelCase__			)			).to(UpperCamelCase__			)
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, self.text_embedder_hidden_size)     , rng=random.Random(seed + 1			)			).to(
           UpperCamelCase__			)
       # create init_image
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, 3, 64, 64)     , rng=random.Random(UpperCamelCase__			)			).to(UpperCamelCase__			)
       SCREAMING_SNAKE_CASE										=   image.cpu().permute(0     , 2     , 3     , 1			)[0]
       SCREAMING_SNAKE_CASE										=   Image.fromarray(np.uinta(UpperCamelCase__			)			).convert("RGB"			).resize((256, 256)			)
       # create hint
       SCREAMING_SNAKE_CASE										=   floats_tensor((1, 3, 64, 64)     , rng=random.Random(UpperCamelCase__			)			).to(UpperCamelCase__			)

       if str(UpperCamelCase__			).startswith("mps"			):
           SCREAMING_SNAKE_CASE										=   torch.manual_seed(UpperCamelCase__			)
       else:
           SCREAMING_SNAKE_CASE										=   torch.Generator(device=UpperCamelCase__			).manual_seed(UpperCamelCase__			)
       SCREAMING_SNAKE_CASE										=   {
           "image": init_image,
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "hint": hint,
           "generator": generator,
           "height": 64,
           "width": 64,
           "num_inference_steps": 10,
           "guidance_scale": 7.0,
           "strength": 0.2,
           "output_type": "np",
       }
       return inputs





   def      _snake_case		(       self					:			Dict			):
       SCREAMING_SNAKE_CASE										=   "cpu"

       SCREAMING_SNAKE_CASE										=   self.get_dummy_components()

       SCREAMING_SNAKE_CASE										=   self.pipeline_class(**UpperCamelCase__			)
       SCREAMING_SNAKE_CASE										=   pipe.to(UpperCamelCase__			)

       pipe.set_progress_bar_config(disable=UpperCamelCase__			)

       SCREAMING_SNAKE_CASE										=   pipe(**self.get_dummy_inputs(UpperCamelCase__			)			)
       SCREAMING_SNAKE_CASE										=   output.images

       SCREAMING_SNAKE_CASE										=   pipe(
           **self.get_dummy_inputs(UpperCamelCase__			)     , return_dict=UpperCamelCase__     , )[0]

       SCREAMING_SNAKE_CASE										=   image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE										=   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       SCREAMING_SNAKE_CASE										=   np.array(
           [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]			)
       assert (
           np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
       ), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"





@slow
@require_torch_gpu
class 			_SCREAMING_SNAKE_CASE (   unittest.TestCase		):







   '''simple docstring'''




   def      _snake_case		(       self					:			Dict			):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





   def      _snake_case		(       self					:			Any			):
       SCREAMING_SNAKE_CASE										=   load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"			)

       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"			)
       SCREAMING_SNAKE_CASE										=   init_image.resize((512, 512)			)

       SCREAMING_SNAKE_CASE										=   load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/hint_image_cat.png"			)
       SCREAMING_SNAKE_CASE										=   torch.from_numpy(np.array(UpperCamelCase__			)			).float() / 255.0
       SCREAMING_SNAKE_CASE										=   hint.permute(2     , 0     , 1			).unsqueeze(0			)

       SCREAMING_SNAKE_CASE										=   "A robot, 4k photo"

       SCREAMING_SNAKE_CASE										=   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-prior"     , torch_dtype=torch.floataa			)
       pipe_prior.to(UpperCamelCase__			)

       SCREAMING_SNAKE_CASE										=   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-controlnet-depth"     , torch_dtype=torch.floataa			)
       SCREAMING_SNAKE_CASE										=   pipeline.to(UpperCamelCase__			)

       pipeline.set_progress_bar_config(disable=UpperCamelCase__			)

       SCREAMING_SNAKE_CASE										=   torch.Generator(device="cpu"			).manual_seed(0			)

       SCREAMING_SNAKE_CASE							,	SCREAMING_SNAKE_CASE										=   pipe_prior(
           UpperCamelCase__     , image=UpperCamelCase__     , strength=0.85     , generator=UpperCamelCase__     , negative_prompt=""     , ).to_tuple()

       SCREAMING_SNAKE_CASE										=   pipeline(
           image=UpperCamelCase__     , image_embeds=UpperCamelCase__     , negative_image_embeds=UpperCamelCase__     , hint=UpperCamelCase__     , generator=UpperCamelCase__     , num_inference_steps=100     , height=512     , width=512     , strength=0.5     , output_type="np"     , )

       SCREAMING_SNAKE_CASE										=   output.images[0]

       assert image.shape == (512, 512, 3)

       assert_mean_pixel_difference(UpperCamelCase__     , UpperCamelCase__			)

from __future__ import annotations

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if b == 0:
    return (1, 0)
  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	a % b   )
  __magic_name__								=			a // b
  return (y, x - k * y)

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m

def a__       (			A_,	A_   ):






  '''simple docstring'''





  ((__magic_name__)	,       (__magic_name__))								=			extended_euclid(A_,	A_   )
  if b < 0:
    __magic_name__								=			(b % n + n) % n
  return b

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__								=			invert_modulo(A_,	A_   ), invert_modulo(A_,	A_   )
  __magic_name__								=			na * na
  __magic_name__								=			ra * x * na + ra * y * na
  return (n % m + m) % m


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='chinese_remainder_theorem', verbose=True)
    testmod(name='chinese_remainder_theorem2', verbose=True)
    testmod(name='invert_modulo', verbose=True)
    testmod(name='extended_euclid', verbose=True)

